Assessing Ice Break-Up Trends in Slave River Delta through Satellite Observations and Random Forest Modeling

• Published in: Remote Sensing
• Main Authors: Ida Moalemi, Homa Kheyrollah Pour, K. Andrea Scott
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2024
• Subjects: Slave River Delta; Great Slave Lake; machine learning; random forest modeling; classification; Science; Q; Slave River
• Online Access: https://doi.org/10.3390/rs16122244; https://doaj.org/article/6dfeb8d0bdbd4256b246b43fcc4d73d2

The seasonal temperature trends and ice phenology in the Great Slave Lake (GSL) are significantly influenced by inflow from the Slave River. The river undergoes a sequence of mechanical break-ups all the way to the GSL, initiating the GSL break-up process. Additionally, upstream water management practices impact the discharge of the Slave River and, consequently, the ice break-up of the GSL. Therefore, monitoring the break-up process at the Slave River Delta (SRD), where the river meets the lake, is crucial for understanding the cascading effects of upstream activities on GSL ice break-up. This research aimed to use Random Forest (RF) models to monitor the ice break-up processes at the SRD using a combination of satellite images with relatively high spatial resolution, including Landsat-5, Landsat-8, Sentinel-2a, and Sentinel-2b. The RF models were trained using selected training pixels to classify ice, open water, and cloud. The onset of break-up was determined by data-driven thresholds on the ice fraction in images with less than 20% cloud coverage. Analysis of break-up timing from 1984 to 2023 revealed a significant earlier trend using the Mann–Kendall test with a p-value of 0.05. Furthermore, break-up data in recent years show a high degree of variability in the break-up rate using images in recent years with better temporal resolution.